SlideShare uma empresa Scribd logo
1 de 115
Baixar para ler offline
Introduction to GAN
서울대학교 방사선의학물리연구실
이 지 민 ( ljm861@gmail.com )
참고 자료 출처 (본 슬라이드 인용 순)
2
좋은 자료를 만들어주신 많은 분들께 다시 한 번 감사의 인사를 전하고 싶고,
슬라이드 좌측 하단에 출처를 명시하였으니, 꼭 찾아보시길 바랍니다. 
0.
•
•
•
•
•
•
•
Contents
3
1. Generative Model
2. Auto-Regressive Models
3. Variational Auto-Encoder
4. Generative Adversarial Networks
5. Significant Variants & Applications
Generative Model
4
Generative Model
5
1.
Generative Model
6
1.
Generative Model
7
1.
Generative Model
8
1.
Generative Model
9
1.
Generative Model
10
Ideal Generative Model
1.
CAT
Short
Hair
Big
Ear
Model
Generative Model
11
Ideal Generative Model
1.
CAT
Short
Hair
Big
Ear
Model
Generative Model
12
Ideal Generative Model
1.
CAT
Short
Hair
Big
Ear
Tabby
Model
Generative Model
13
Ideal Generative Model
1.
CAT
Short
Hair
Big
Ear
Tabby
Savannah Cat
Model
Generative Model
14
1.
Generative Model
15
1.
https://www.slideshare.net/carpedm20/pycon-korea-2016 (지적 대화를 위한 깊고 넓은 딥러닝, 김태훈)
Generative Model
16
1.
https://www.slideshare.net/carpedm20/pycon-korea-2016 (지적 대화를 위한 깊고 넓은 딥러닝, 김태훈)
Generative Model
17
1.
https://www.slideshare.net/carpedm20/pycon-korea-2016 (지적 대화를 위한 깊고 넓은 딥러닝, 김태훈)
Generative Model
18
1.
https://www.slideshare.net/carpedm20/pycon-korea-2016 (지적 대화를 위한 깊고 넓은 딥러닝, 김태훈)
Generative Model
19
1.
https://www.slideshare.net/carpedm20/pycon-korea-2016 (지적 대화를 위한 깊고 넓은 딥러닝, 김태훈)
Generative Model
20
1.
https://blog.openai.com/generative-models/
Generative Model
21
Why Generative Model
1.
•
•
•
•
•
•
Li, Yijun, et al., Generative face completion, 2017
Generative Model
22
Deep Generative Models
1.
 Auto-Regressive Models
 Variational Auto-Encoder
 Generative Adversarial Networks
Auto-Regressive Models
23
Auto-Regressive Models
24
Pixel-by-pixel generation
2.
http://slazebni.cs.illinois.edu/spring17/lec13_advanced.pdf
Auto-Regressive Models
25
Multi-Dimensional RNNs (2013)
2.
Graves et al, Multi-Dimensional Recurrent Neural Networks, 2013
Auto-Regressive Models
26
Spatial LSTM (2015)
2.
Theis et al., Generative Image Modeling Using Spatial LSTMs, 2015
Auto-Regressive Models
27
Pixel RNN (2016)
2.
Aaron et al, Pixel Recurrent Neural Networks, 2016
Auto-Regressive Models
28
Sampling
2.
•
➔
•
➔
•
➔
•
•
Auto-Regressive Models
29
Sampling
2.
Auto-Regressive Models
30
Sampling
2.
Auto-Regressive Models
31
Sampling
2.
Auto-Regressive Models
32
Sampling
2.
Auto-Regressive Models
33
Pixel RNN (2016)
2.
Aaron et al, Pixel Recurrent Neural Networks, 2016
Auto-Regressive Models
34
Features
2.
•
•
•
•
Variational Auto-Encoder
35
Variational Auto-Encoder
36
Auto-Encoder
3.
http://kvfrans.com/variational-autoencoders-explained/
Variational Auto-Encoder
37
Auto-Encoder
3.
http://kvfrans.com/variational-autoencoders-explained/
?
Variational Auto-Encoder
38
Variational Auto-Encoder
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf
Variational Auto-Encoder
39
Variational Auto-Encoder
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
40
Variational Auto-Encoder
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
41
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
42
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
43
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
44
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
45
3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
46
Kullback-Leibler Divergence
3.
https://en.wikipedia.org/wiki/Kullback–Leibler_divergence
Variational Auto-Encoder
47
Loss function
3.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)
Variational Auto-Encoder
48
Reparameterization Trick
3.
Carl Doersch, Tutorial on Variational Autoencoders, 2016
Variational Auto-Encoder
49
Results
3.
Kingma et al., Auto-Encoding Variational Bayes, 2014
Variational Auto-Encoder
50
Results
3.
https://www.slideshare.net/HyungjooCho2/deep-generative-modelpdf (Deep Generative Models, 조형주)
Variational Auto-Encoder
51
Features
3.
•
•
•
•
Generative Adversarial Networks
52
Generative Adversarial Networks
53
4.
Generative Adversarial Networks
54
4.
생성 모델
Generative Adversarial Networks
55
4.
적대적 학습
Generative Adversarial Networks
56
4.
적대적 학습
Generator vs Discriminator
Generative Adversarial Networks
57
4.
https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
Generative Adversarial Networks
58
4.
https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091 (Generative Adversarial Networks, 김남주)
Generative Adversarial Networks
59
Value Function
4.
Generative Adversarial Networks
60
Value Function
4.
https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network (1시간 만에 GAN 완전 정복하기, 최윤제)
Generative Adversarial Networks
61
Value Function
4.
https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network (1시간 만에 GAN 완전 정복하기, 최윤제)
Generative Adversarial Networks
62
4.
Goodfellow et al, Generative Adversarial Networks, 2014
Generative Adversarial Networks
63
Results
4.
Goodfellow et al, Generative Adversarial Networks, 2014
Generative Adversarial Networks
64
Features
4.
•
•
•
•
Generative Adversarial Networks
65
Comparison with Auto-regressive models and VAE
4.
http://slazebni.cs.illinois.edu/spring17/lec13_advanced.pdf, https://openai.com/blog/generative-models/
Generative Adversarial Networks
66
DCGAN (Deep Convolutional GAN)
4.
Radford et al, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015
Generative Adversarial Networks
67
DCGAN (Deep Convolutional GAN)
4.
Radford et al, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015 / https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6
Generative Adversarial Networks
68
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
69
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
70
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
71
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
72
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
73
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
74
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
75
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
76
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
77
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
78
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
79
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
80
Pytorch Implementation
4.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)
Generative Adversarial Networks
81
Issues during Training
4.
•
•
•
•
•
Generative Adversarial Networks
82
Mode collapsing / Oscillating
Metz et al, Unrolled Generative Adversarial Networks, 2016
4.
Generative Adversarial Networks
83
Mode collapsing / Oscillating
4.
https://www.slideshare.net/HyungjooCho2/deep-generative-modelpdf (Deep Generative Models, 조형주)
Generative Adversarial Networks
84
Mode collapsing / Oscillating
4.
https://www.slideshare.net/HyungjooCho2/deep-generative-modelpdf (Deep Generative Models, 조형주)
Generative Adversarial Networks
85
Mode collapsing / Oscillating
4.
https://www.slideshare.net/HyungjooCho2/deep-generative-modelpdf (Deep Generative Models, 조형주)
Generative Adversarial Networks
86
Mode collapsing / Oscillating
4.
Target MLE (=KL) JS Reverse KL
Generative Adversarial Networks
87
Mode collapsing / Oscillating
4.
Metz et al, Unrolled Generative Adversarial Networks, 2016
Generative Adversarial Networks
88
Intractable loss
4.
https://www.slideshare.net/ssuser7e10e4/wasserstein-gan-i (Wasserstein GAN 수학 이해하기, 임성빈)
Generative Adversarial Networks
89
Intractable loss
4.
https://www.slideshare.net/ssuser7e10e4/wasserstein-gan-i (Wasserstein GAN 수학 이해하기, 임성빈)
Generative Adversarial Networks
90
Intractable loss
4.
https://www.slideshare.net/ssuser7e10e4/wasserstein-gan-i (Wasserstein GAN 수학 이해하기, 임성빈)
Generative Adversarial Networks
91
Intractable loss
4.
Vanilla GAN
LSGAN WGAN
Generative Adversarial Networks
92
Intractable loss
4.
Arjovsky et al, Wasserstein Generative Adversarial Networks, 2017
Generative Adversarial Networks
93
Intractable loss
4.
Arjovsky et al, Wasserstein Generative Adversarial Networks, 2017
Generative Adversarial Networks
94
Balance between Generator & Discriminator
4.
Berthelot et al, BEGAN, 2017
Generative Adversarial Networks
95
Manipulation
4.
Mirza et al, Conditional Generative Adversarial Networks, 2014
Generative Adversarial Networks
96
Quality
4.
Karras et al, Progressive Growing of GANs For Improved Quality, Stability, and Variation, 2017
Generative Adversarial Networks
97
Quality
4.
https://www.youtube.com/watch?v=XOxxPcy5Gr4
Generative Adversarial Networks
98
Quality
4.
Karras et al, Progressive Growing of GANs For Improved Quality, Stability, and Variation, 2017
Significant Variants & Applications
99
Significant Variants
100
Info GAN
5.
Chen et al, InfoGAN, 2017
Significant Variants
101
Pix2Pix
5.
Isola et al, Image-to-image translation with conditional GAN, 2016
Significant Variants
102
Domain Cross GAN
5.
Taigman et al, Unsupervised Cross-Domain Image Generation, 2016
Significant Variants
103
CycleGAN
5.
Zhu et al, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, 2017
Significant Variants
104
CycleGAN
5.
Zhu et al, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, 2017
Significant Variants
105
DiscoGAN
5.
Kim et al, Learning to Discover Cross Domain Relations with Generative Adversarial Networks, 2017
Applications
106
Time Series Generation (InfoGAN)
5.
https://github.com/buriburisuri/timeseries_gan (김남주)
Applications
107
ehrGAN
5.
Che et al., Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records, 2017
Applications
108
RCGAN
5.
Che et al., Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records, 2017
Applications
109
GAN for Low-dose CT
5.
Jelmer M. et al, Generative Adversarial Networks for Noise Reduction in Low-Dose CT, 2017, http://medicine.utah.edu/radiology/news/2016/low-dose-ct-zeng-award.php
Applications
110
GAN for Low-dose CT
5.
Jelmer M. et al, Generative Adversarial Networks for Noise Reduction in Low-Dose CT, 2017
Applications
111
Stain Style Transfer
5.
H Cho et al, Neural Stain-Style Transfer Learning using GAN for Histopathological Images, 2017
Applications
112
Simulated & Unsupervised Learning
5.
Shrivastava et al, Learning from Simulated and Unsupervised Images through Adversarial Training, 2016
Applications
113
AnoGAN
5.
Schlegl et al, Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker discovery, 2017
Q & A
114
감사합니다.
115

Mais conteúdo relacionado

Semelhante a Introduction to GAN

(Very) Recent AI advances for Chemical Engineering research and education
(Very) Recent AI advances for Chemical Engineering research and education(Very) Recent AI advances for Chemical Engineering research and education
(Very) Recent AI advances for Chemical Engineering research and educationRichard West
 
Prototype design patterns
Prototype design patternsPrototype design patterns
Prototype design patternsThaichor Seng
 
SpringOne Platform recap 정윤진
SpringOne Platform recap 정윤진SpringOne Platform recap 정윤진
SpringOne Platform recap 정윤진VMware Tanzu Korea
 
GNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.pptGNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.pptManiMaran230751
 
The deep bootstrap framework review
The deep bootstrap framework reviewThe deep bootstrap framework review
The deep bootstrap framework reviewtaeseon ryu
 
Mockito vs JMockit, battle of the mocking frameworks
Mockito vs JMockit, battle of the mocking frameworksMockito vs JMockit, battle of the mocking frameworks
Mockito vs JMockit, battle of the mocking frameworksEndranNL
 
Variants of GANs - Jaejun Yoo
Variants of GANs - Jaejun YooVariants of GANs - Jaejun Yoo
Variants of GANs - Jaejun YooJaeJun Yoo
 
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...Pluribus One
 
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network ModelsActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network ModelsMinsuk Kahng
 
Building Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning Talks
Building Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning TalksBuilding Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning Talks
Building Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning TalksAtlassian
 
Automated Software Enging, Fall 2015, NCSU
Automated Software Enging, Fall 2015, NCSUAutomated Software Enging, Fall 2015, NCSU
Automated Software Enging, Fall 2015, NCSUCS, NcState
 
New Ideas for Old Code - Greach
New Ideas for Old Code - GreachNew Ideas for Old Code - Greach
New Ideas for Old Code - GreachHamletDRC
 
PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...
PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...
PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...Puppet
 
AI Food detector; A model of Generative adversarial network for food Classifier
AI Food detector; A model of Generative adversarial network for food ClassifierAI Food detector; A model of Generative adversarial network for food Classifier
AI Food detector; A model of Generative adversarial network for food Classifierjimmy majumder
 
All about unit testing using (power) mock
All about unit testing using (power) mockAll about unit testing using (power) mock
All about unit testing using (power) mockPranalee Rokde
 
Breaking Dependencies Legacy Code - Cork Software Crafters - September 2019
Breaking Dependencies Legacy Code -  Cork Software Crafters - September 2019Breaking Dependencies Legacy Code -  Cork Software Crafters - September 2019
Breaking Dependencies Legacy Code - Cork Software Crafters - September 2019Paulo Clavijo
 

Semelhante a Introduction to GAN (20)

groovy & grails - lecture 7
groovy & grails - lecture 7groovy & grails - lecture 7
groovy & grails - lecture 7
 
(Very) Recent AI advances for Chemical Engineering research and education
(Very) Recent AI advances for Chemical Engineering research and education(Very) Recent AI advances for Chemical Engineering research and education
(Very) Recent AI advances for Chemical Engineering research and education
 
Prototype design patterns
Prototype design patternsPrototype design patterns
Prototype design patterns
 
SpringOne Platform recap 정윤진
SpringOne Platform recap 정윤진SpringOne Platform recap 정윤진
SpringOne Platform recap 정윤진
 
Gan intro
Gan introGan intro
Gan intro
 
GNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.pptGNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.ppt
 
The deep bootstrap framework review
The deep bootstrap framework reviewThe deep bootstrap framework review
The deep bootstrap framework review
 
Automate Design Patterns
Automate Design PatternsAutomate Design Patterns
Automate Design Patterns
 
Mockito vs JMockit, battle of the mocking frameworks
Mockito vs JMockit, battle of the mocking frameworksMockito vs JMockit, battle of the mocking frameworks
Mockito vs JMockit, battle of the mocking frameworks
 
Variants of GANs - Jaejun Yoo
Variants of GANs - Jaejun YooVariants of GANs - Jaejun Yoo
Variants of GANs - Jaejun Yoo
 
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning. ICMLC 201...
 
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network ModelsActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
 
Building Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning Talks
Building Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning TalksBuilding Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning Talks
Building Atlassian Plugins with Groovy - Atlassian Summit 2010 - Lightning Talks
 
Automated Software Enging, Fall 2015, NCSU
Automated Software Enging, Fall 2015, NCSUAutomated Software Enging, Fall 2015, NCSU
Automated Software Enging, Fall 2015, NCSU
 
New Ideas for Old Code - Greach
New Ideas for Old Code - GreachNew Ideas for Old Code - Greach
New Ideas for Old Code - Greach
 
PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...
PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...
PuppetConf 2016: Turning Pain Into Gain: A Unit Testing Story – Nadeem Ahmad ...
 
AI Food detector; A model of Generative adversarial network for food Classifier
AI Food detector; A model of Generative adversarial network for food ClassifierAI Food detector; A model of Generative adversarial network for food Classifier
AI Food detector; A model of Generative adversarial network for food Classifier
 
Advanced Java Testing
Advanced Java TestingAdvanced Java Testing
Advanced Java Testing
 
All about unit testing using (power) mock
All about unit testing using (power) mockAll about unit testing using (power) mock
All about unit testing using (power) mock
 
Breaking Dependencies Legacy Code - Cork Software Crafters - September 2019
Breaking Dependencies Legacy Code -  Cork Software Crafters - September 2019Breaking Dependencies Legacy Code -  Cork Software Crafters - September 2019
Breaking Dependencies Legacy Code - Cork Software Crafters - September 2019
 

Último

Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDELiveplex
 
Governance in SharePoint Premium:What's in the box?
Governance in SharePoint Premium:What's in the box?Governance in SharePoint Premium:What's in the box?
Governance in SharePoint Premium:What's in the box?Juan Carlos Gonzalez
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024D Cloud Solutions
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1DianaGray10
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostMatt Ray
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Brian Pichman
 
VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXTarek Kalaji
 
UiPath Studio Web workshop series - Day 5
UiPath Studio Web workshop series - Day 5UiPath Studio Web workshop series - Day 5
UiPath Studio Web workshop series - Day 5DianaGray10
 
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...Daniel Zivkovic
 
UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"
UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"
UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"DianaGray10
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaborationbruanjhuli
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfDianaGray10
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024SkyPlanner
 

Último (20)

Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
 
Governance in SharePoint Premium:What's in the box?
Governance in SharePoint Premium:What's in the box?Governance in SharePoint Premium:What's in the box?
Governance in SharePoint Premium:What's in the box?
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )
 
VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBX
 
UiPath Studio Web workshop series - Day 5
UiPath Studio Web workshop series - Day 5UiPath Studio Web workshop series - Day 5
UiPath Studio Web workshop series - Day 5
 
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
All in AI: LLM Landscape & RAG in 2024 with Mark Ryan (Google) & Jerry Liu (L...
 
UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"
UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"
UiPath Clipboard AI: "A TIME Magazine Best Invention of 2023 Unveiled"
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024
 

Introduction to GAN